Advanced Baseline for 3D Human Pose Estimation: A Two-Stage Approach
This work addresses 3D human pose estimation for computer vision applications, but it is incremental as it builds on existing solutions.
The paper tackled the problem of 3D human pose estimation by proposing an advanced baseline model for the 2D-to-3D lifting process in two-stage methods, resulting in satisfactory performance on the Human3.6M benchmark.
Human pose estimation has been widely applied in various industries. While recent decades have witnessed the introduction of many advanced two-dimensional (2D) human pose estimation solutions, three-dimensional (3D) human pose estimation is still an active research field in computer vision. Generally speaking, 3D human pose estimation methods can be divided into two categories: single-stage and two-stage. In this paper, we focused on the 2D-to-3D lifting process in the two-stage methods and proposed a more advanced baseline model for 3D human pose estimation, based on the existing solutions. Our improvements include optimization of machine learning models and multiple parameters, as well as introduction of a weighted loss to the training model. Finally, we used the Human3.6M benchmark to test the final performance and it did produce satisfactory results.